cv_buffer function

Use buffer around records to separate train and test folds (a.k.a. buffered/spatial leave-one-out)

This function generates spatially separated train and test folds by considering buffers of the specified distance (size parameter) around each observation point. This approach is a form of leave-one-out cross-validation. Each fold is generated by excluding nearby observations around each testing point within the specified distance (ideally the range of spatial autocorrelation, see cv_spatial_autocor). In this method, the testing set never directly abuts a training sample (e.g. presence or absence; 0s and 1s). For more information see the details section.

cv_buffer(
  x,
  column = NULL,
  size,
  presence_bg = FALSE,
  add_bg = FALSE,
  progress = TRUE,
  report = TRUE
)

Arguments

• x: a simple features (sf) or SpatialPoints object of spatial sample data (e.g., species data or ground truth sample for image classification).
• column: character; indicating the name of the column in which response variable (e.g. species data as a binary response i.e. 0s and 1s) is stored. This is required when presence_bg = TRUE, otherwise optional.
• size: numeric value of the specified range by which training/testing data are separated. This distance should be in metres . The range could be explored by cv_spatial_autocor.
• presence_bg: logical; whether to treat data as species presence-background data. For all other data types (presence-absence, continuous, count or multi-class responses), this option should be FALSE.
• add_bg: logical; add background points to the test set when presence_bg = TRUE. We do not recommend this according to Radosavljevic & Anderson (2014). Keep it FALSE, unless you mean to add the background pints to testing points.
• progress: logical; whether to shows a progress bar.
• report: logical; whether to generate print summary of records in each fold; for very big datasets, set to FALSE for faster calculation.

Returns

An object of class S3. A list of objects including:

• folds_list - a list containing the folds. Each fold has two vectors with the training (first) and testing (second) indices
• k - number of the folds
• size - the defined range of spatial autocorrelation)
• column - the name of the column if provided
• presence_bg - whether this was treated as presence-background data
• records - a table with the number of points in each category of training and testing

Details

When working with presence-background (presence and pseudo-absence) species distribution data (should be specified by presence_bg = TRUE argument), only presence records are used for specifying the folds (recommended). Consider a target presence point. The buffer is defined around this target point, using the specified range (size). By default, the testing fold comprises only the target presence point (all background points within the buffer are also added when add_bg = TRUE). Any non-target presence points inside the buffer are excluded. All points (presence and background) outside of buffer are used for the training set. The methods cycles through all the presence data, so the number of folds is equal to the number of presence points in the dataset.

For presence-absence data (and all other types of data), folds are created based on all records, both presences and absences. As above, a target observation (presence or absence) forms a test point, all presence and absence points other than the target point within the buffer are ignored, and the training set comprises all presences and absences outside the buffer. Apart from the folds, the number of training-presence, training-absence, testing-presence and testing-absence

records is stored and returned in the records table. If column = NULL and presence_bg = FALSE, the procedure is like presence-absence data. All other data types (continuous, count or multi-class responses) should be done by presence_bg = FALSE.

Examples

library(blockCV)

# import presence-absence species data
points <- read.csv(system.file("extdata/", "species.csv", package = "blockCV"))
# make an sf object from data.frame
pa_data <- sf::st_as_sf(points, coords = c("x", "y"), crs = 7845)

bloo <- cv_buffer(x = pa_data,
                  column = "occ",
                  size = 350000, # size in metres no matter the CRS
                  presence_bg = FALSE)

References

Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: Complexity, overfitting and evaluation. Journal of Biogeography, 41, 629–643. https://doi.org/10.1111/jbi.12227

See Also

cv_nndm, cv_spatial, and cv_spatial_autocor